Tariq Taimoor, Satti Muhammad Hashim, Saeed Maryam, Kamboh Awais Mehmood
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1074-1077. doi: 10.1109/EMBC.2017.8037013.
Real time on-chip spike detection is the first step in decoding neural spike trains in implantable brain machine interface systems. Nonlinear Energy Operator (NEO) is a transform widely used to distinguish neural spikes from background noise. In this paper we define a general form of energy operators, of which NEO is a specific example, which gives better spike-noise separation than NEO and its derivatives. This is because of a non-linear scaling applied to the general discrete energy operator. Using two well-known publically available datasets, the performance of several operators is compared. On data sets that contain multi-unit spikes with low Signal to Noise ratio, the detection accuracy was improved by approximately 15%.
实时片上脉冲检测是可植入脑机接口系统中解码神经脉冲序列的第一步。非线性能量算子（NEO）是一种广泛用于从背景噪声中区分神经脉冲的变换。在本文中，我们定义了一种能量算子的通用形式，NEO是其一个具体示例，该通用形式比NEO及其派生形式能实现更好的脉冲-噪声分离。这是因为对通用离散能量算子应用了非线性缩放。使用两个著名的公开可用数据集，比较了几种算子的性能。在包含低信噪比多单元脉冲的数据集上，检测准确率提高了约15%。
